Psychometric and adherence considerations for high-frequency, smartphone-based cognitive screening protocols in older adults.

OBJECTIVE: The psychometric rigor of unsupervised, smartphone-based assessments and factors that impact remote protocol engagement is critical to evaluate prior to the use of such methods in clinical contexts. We evaluated the validity of a high-frequency, smartphone-based cognitive assessment protocol, including examining convergence and divergence with standard cognitive tests, and investigating factors that may impact adherence and performance (i.e., time of day and anticipated receipt of feedback vs. no feedback). METHODS: Cognitively unimpaired participants (N = 120, Mage = 68.8, 68.3% female, 87% White, Meducation = 16.5 years) completed 8 consecutive days of the Mobile Monitoring of Cognitive Change (M2C2), a mobile app-based testing platform, with brief morning, afternoon, and evening sessions. Tasks included measures of working memory, processing speed, and episodic memory. Traditional neuropsychological assessments included measures from the Preclinical Alzheimer's Cognitive Composite battery. RESULTS: Findings showed overall high compliance (89.3%) across M2C2 sessions. Average compliance by time of day ranged from 90.2% for morning sessions, to 77.9% for afternoon sessions, and 84.4% for evening sessions. There was evidence of faster reaction time and among participants who expected to receive performance feedback. We observed excellent convergent and divergent validity in our comparison of M2C2 tasks and traditional neuropsychological assessments. CONCLUSIONS: This study supports the validity and reliability of self-administered, high-frequency cognitive assessment via smartphones in older adults. Insights into factors affecting adherence, performance, and protocol implementation are discussed.

Availability of healthy foods, fruit and vegetable consumption, and cognition among urban older adults.

BACKGROUND: . Although prior studies have examined the associations between neighborhood characteristics and cognitive health, little is known about whether local food environments, which are critical for individuals' daily living, are associated with late-life cognition. Further, little is known about how local environments may shape individuals' health-related behaviors and impact cognitive health. The aim of this study is to examine whether objective and subjective measures of healthy food availability are associated with ambulatory cognitive performance and whether behavioral and cardiovascular factors mediate these associations among urban older adults. METHODS: . The sample consisted of systematically recruited, community-dwelling older adults (N = 315, mean age = 77.5, range = 70-91) from the Einstein Aging Study. Objective availability of healthy foods was defined as density of healthy food stores. Subjective availability of healthy foods and fruit/vegetable consumption were assessed using self-reported questionnaires. Cognitive performance was assessed using smartphone-administered cognitive tasks that measured processing speed, short-term memory binding, and spatial working memory performance 6 times a day for 14 days. RESULTS: . Results from multilevel models showed that subjective availability of healthy foods, but not objective food environments, was associated with better processing speed (estimate= -0.176, p = .003) and more accurate memory binding performance (estimate = 0.042, p = .012). Further, 14~16% of the effects of subjective availability of healthy foods on cognition were mediated through fruit and vegetable consumption. CONCLUSIONS: . Local food environments seem to be important for individuals' dietary behavior and cognitive health. Specifically, subjective measures of food environments may better reflect individuals' experiences regarding their local food environments not captured by objective measures. Future policy and intervention strategies will need to include both objective and subjective food environment measures in identifying impactful target for intervention and evaluating effectiveness of policy changes.

To BYOD or not: Are device latencies important for bring-your-own-device (BYOD) smartphone cognitive testing?

Studies using remote cognitive testing must make a critical decision: whether to allow participants to use their own devices or to provide participants with a study-specific device. Bring-your-own-device (BYOD) studies have several advantages including increased accessibility, potential for larger sample sizes, and reduced participant burden. However, BYOD studies offer little control over device performance characteristics that could potentially influence results. In particular, response times measured by each device not only include the participant's true response time, but also latencies of the device itself. The present study investigated two prominent sources of device latencies that pose significant risks to data quality: device display output latency and touchscreen input latency. We comprehensively tested 26 popular smartphones ranging in price from < $100 to $1000+ running either Android or iOS to determine if hardware and operating system differences led to appreciable device latency variability. To accomplish this, a custom-built device called the Latency and Timing Assessment Robot (LaTARbot) measured device display output and capacitive touchscreen input latencies. We found considerable variability across smartphones in display and touch latencies which, if unaccounted for, could be misattributed as individual or group differences in response times. Specifically, total device (sum of display and touch) latencies ranged from 35 to 140 ms. We offer recommendations to researchers to increase the precision of data collection and analysis in the context of remote BYOD studies.

Arterial Stiffness and Cardiorespiratory Fitness Are Associated With Cognitive Function in Older Adults.

Age-related cognitive impairment has been associated with arterial stiffening and decreased cardiorespiratory fitness. The aims of this cross-sectional study were to compare cognitive function domains and cardiovascular parameters in older adults (≥ 65 years old) with high and normal aortic stiffness (via carotid-femoral pulse wave velocity, cfPWV) and to explore relationships among cfPWV, carotid intima-media thickness, cardiorespiratory fitness, and cognitive function. Vascular and cognitive function were measured in older adults with either normal or high cfPWV. Cognitive function was measured via an intensive one-time neuropsychological battery, while cfPWV by applanation tonometry, carotid intima-media thickness and function (i.e., distensibility) by ultrasonography, and cardiorespiratory fitness (i.e., VO2peak) by a submaximal exercise test. Correlations among age, VO2peak, carotid intima-media thickness, cfPWV, and cognitive function were performed along with a series of multivariate analyses of variance. Compared with NAS, participants with HAS had greater aortic, carotid, and brachial blood pressures but similar cardiorespiratory fitness and carotid intima-media thickness and distensibility. Participants with NAS exhibited better neuropsychological performance in executive function and attention and overall cognitive function than those with HAS. When controlling for age, visual scanning and perception scores were correlated with cfPWV and VO2peak. Our findings suggest that certain cognitive domains for older adults are associated with their cardiorespiratory fitness and aortic stiffness.

A Machine-Learning Based Approach for Predicting Older Adults' Adherence to Technology-Based Cognitive Training.

Adequate adherence is a necessary condition for success with any intervention, including for computerized cognitive training designed to mitigate age-related cognitive decline. Tailored prompting systems offer promise for promoting adherence and facilitating intervention success. However, developing adherence support systems capable of just-in-time adaptive reminders requires understanding the factors that predict adherence, particularly an imminent adherence lapse. In this study we built machine learning models to predict participants' adherence at different levels (overall and weekly) using data collected from a previous cognitive training intervention. We then built machine learning models to predict adherence using a variety of baseline measures (demographic, attitudinal, and cognitive ability variables), as well as deep learning models to predict the next week's adherence using variables derived from training interactions in the previous week. Logistic regression models with selected baseline variables were able to predict overall adherence with moderate accuracy (AUROC: 0.71), while some recurrent neural network models were able to predict weekly adherence with high accuracy (AUROC: 0.84-0.86) based on daily interactions. Analysis of the post hoc explanation of machine learning models revealed that general self-efficacy, objective memory measures, and technology self-efficacy were most predictive of participants' overall adherence, while time of training, sessions played, and game outcomes were predictive of the next week's adherence. Machine-learning based approaches revealed that both individual difference characteristics and previous intervention interactions provide useful information for predicting adherence, and these insights can provide initial clues as to who to target with adherence support strategies and when to provide support. This information will inform the development of a technology-based, just-in-time adherence support systems.

Aging: Older Adults' Driving Behavior Using Longitudinal and Lateral Warning Systems.

OBJECTIVE: This study assessed older drivers' driving behavior when using longitudinal and lateral vehicle warning systems together. BACKGROUND: Advanced driver assistance systems (ADAS) can benefit drivers of all ages. Previous research with younger to middle-aged samples suggests that safety benefits are not necessarily additive with additional ADAS. Increases in following distance associated with the use of forward collision warning (FCW) decreased when drivers also used lane departure warning (LDW), likely due to attending to the LDW more than the FCW. METHOD: The current study used a driving simulator to provide 128 older drivers experience with FCW and/or LDW system(s) during a ~25-min drive to gauge their usage's effects on driving performance and subjective workload. RESULTS: There were no significant differences found in headway distance between older drivers who used different combinations of FCW and LDW systems, but those who used an FCW system showed significantly longer time-to-collision (TTC) when approaching the critical event than those who did not. Users of LDW systems did not show reductions in standard deviation of lane position. Analyses of subjective workload measures showed no significant differences between conditions. CONCLUSION: Findings suggest that FCW could increase older drivers' TTC over the course of a drive. Contrary to previous findings in younger samples, concurrent use of FCW and LDW systems did not adversely affect older drivers' longitudinal driving performance and subjective workload. APPLICATION: Potential applications of this research include the assessment of older drivers' use of vehicle warning systems and their effects on subjective workload.

Comparing the effectiveness of two theory-based strategies to promote cognitive training adherence.

This study compared the effectiveness of two theory-based strategies to promote cognitive training adherence among older adults (Mage = 70 years, SD = 4.42, range = 64-84). Strategies incorporated either (a) elements of implementation intention formation or (b) positive message framing, both of which have been found to promote adherence to health behaviors in other domains. Participants (N = 120) were asked to engage in technology-based cognitive training at home comprised of seven gamified neuropsychological tasks. In Phase 1 (structured), participants were provided a schedule that required engagement in 1 hr of cognitive training 5 days each week over 2 months. In Phase 2 (unstructured), participants were instructed to engage with the intervention as much as they desired for 1 month. Contrary to expectations, neither the implementation intention nor positive message framing produced greater adherence relative to control as measured by the total number of training sessions completed in each phase. However, exploratory analysis indicated a greater likelihood of intervention engagement for participants assigned to the implementation intention condition on many days of the intervention, though the trajectory of engagement decline was similar for all three groups. Measures of cognition, attitudes/personality, and technology proficiency also did not predict adherence over either phase. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Ultra-brief Assessment of Working Memory Capacity: Ambulatory Assessment Study Using Smartphones.

BACKGROUND: The development of mobile technology with substantial computing power (ie, smartphones) has enabled the adaptation of performance-based cognitive assessments to remote administration and novel intensive longitudinal study designs (eg, measurement burst designs). Although an "ambulatory" cognitive assessment paradigm may provide new research opportunities, the adaptation of conventional measures to a mobile format conducive to intensive repeated measurement involves balancing measurement precision, administration time, and procedural consistency. OBJECTIVE: Across 3 studies, we adapted "complex span" tests of working memory capacity (WMC) for ultra-brief, smartphone-based administration and examined their reliability, sufficiency, and associations with full-length, laboratory-based computerized administrations. METHODS: In a laboratory-based setting, study 1 examined associations between ultra-brief smartphone adaptations of the operation span, symmetry span, and rotation span tasks and full-length computerized versions. In study 2, we conducted a 4-day ecological momentary assessment (EMA) study (4 assessments per day), where we examined the reliability of ultra-brief, ambulatory administrations of each task. In study 3, we conducted a 7-day EMA study (5 assessments per day) involving the ultra-brief rotation span task, where we examined reliability in the absence of extensive onboarding and training. RESULTS: Measurement models in study 1 suggest that comparable estimates of latent WMC can be recovered from ultra-brief complex span task performance on smartphones. Significant correlations between the ultra-brief tasks and respective full-length versions were observed in study 1 and 2, ranging from r=0.4 to r=0.57. Results of study 2 and study 3 suggest that reliable between-person estimates of operation span, symmetry span, rotation span, and latent WMC can be obtained in 2-3 ultra-brief administrations (equivalent to <1 day of testing in an EMA study design). The results of study 3 replicated our findings, showing that reliable between-person estimates of rotation span may be obtained in as few as 2 ultra-brief administrations in the absence of extensive onboarding and training. In addition, the modification of task parameterization for study 3 improved the estimates of reliability of within-person change. CONCLUSIONS: Ultra-brief administration of complex span tasks on smartphones in a measurement burst design can generate highly reliable cross-sectional estimates of WMC. Considerations for future mobile cognitive assessment designs and parameterizations are discussed.

Assessing satisfaction of search in virtual mammograms for experienced and novice searchers.

Purpose: Satisfaction of search (SOS) is a phenomenon where searchers are more likely to miss a lesion/target after detecting a first lesion/target. Here, we investigated SOS for masses and calcifications in virtual mammograms with experienced and novice searchers to determine the extent to which: (1) SOS affects breast lesion detection, (2) similarity between lesions impacts detection, and (3) experience impacts SOS rates. Approach: The open virtual clinical trials framework was used to simulate the breast anatomy of patients, and up to two simulated masses and/or single-calcifications were inserted into the breast models. Experienced searchers (residents, fellows, and radiologists with breast imaging experience) and novice searchers (undergraduates who had no breast imaging experience) were instructed to search for up to two lesions (masses and calcifications) per image. Results: 2×2 mixed factors analysis of variances (ANOVAs) were run with: (1) single versus second lesion hit rates, (2) similar versus dissimilar second-lesion hit rates, and (3) similar versus dissimilar second-lesion response times as within-subject factors and experience as the between subject's factor. The ANOVAs demonstrated that: (1) experienced and novice searchers made a significant amount of SOS errors, (2) similarity had little impact on experienced searchers, but novice searchers were more likely to miss a dissimilar second lesion compared to when it was similar to a detected first lesion, (3) experienced and novice searchers were faster at finding similar compared to dissimilar second lesions. Conclusions: We demonstrated that SOS is a significant cause of lesion misses in virtual mammograms and that reader experience impacts detection rates for similar compared to dissimilar abnormalities. These results suggest that experience may impact strategy and/or recognition with theoretical implications for determining why SOS occurs.

Remote and in-clinic digital cognitive screening tools outperform the MoCA to distinguish cerebral amyloid status among cognitively healthy older adults.

INTRODUCTION: We evaluated the accuracy of remote and in-person digital tests to distinguish between older adults with and without AD pathological change and used the Montreal Cognitive Assessment (MoCA) as a comparison test. METHODS: Participants were 69 cognitively normal older adults with known beta-amyloid (Aß) PET status. Participants completed smartphone-based assessments 3×/day for 8 days, followed by TabCAT tasks, DCTclock™, and MoCA at an in-person study visit. We calculated the area under the curve (AUC) to compare task accuracies to distinguish Aß status. RESULTS: Average performance on the episodic memory (Prices) smartphone task showed the highest accuracy (AUC = 0.77) to distinguish Aß status. On in-person measures, accuracy to distinguish Aß status was greatest for the TabCAT Favorites task (AUC = 0.76), relative to the DCTclockTM (AUC = 0.73) and MoCA (AUC = 0.74). DISCUSSION: Although further validation is needed, our results suggest that several digital assessments may be suitable for more widespread cognitive screening application.

The Effects of Acute Exercise on Driving and Executive Functions in Healthy Older Adults.

The benefits of exercise on cognitive functioning in older adults are well recognized. One limitation of the current literature is that researchers have almost exclusively relied on well-controlled laboratory tasks to assess cognition. Moreover, the effects of a single bout of aerobic exercise in older adults have received limited attention. The proposed study addresses these limitations by assessing the effects of a single bout of exercise on a more ecologically valid task - driving. Seventy-one participants (M age = 66.39 ± 4.70 years) were randomly allocated to 20min cycling at moderate intensity or sitting and watching driving videos. Participants were then tested on their driving performance using a driving simulator. Driving performance was measured with three different scenarios assessing decision making, driving errors, reaction time, and attention. On a subsequent session, all participants were tested on executive functioning before and after a fitness test. Non-significant effects of exercise were observed on driving performance. However, participants performed better on the Trail Making Test (Cohen's d = 0.25) and Stroop test (d = 0.50) after the fitness test compared to their baseline. These results suggest that post-exercise cognitive improvements do not transfer to improved driving performance among healthy older adults. This study also highlights the importance of assessing expectations as a possible moderator of the effects of acute exercise on activities of daily living. Future studies must examine other relevant ecologically valid tasks and ensure similar expectations between experimental and control groups to further advance the knowledge base in the field.

Relative effectiveness of general versus specific cognitive training for aging adults.

In the present study, we examined three experimental cognitive interventions, two targeted at training general cognitive abilities and one targeted at training specific instrumental activities of daily living (IADL) abilities, along with one active control group to compare benefits of these interventions beyond expectation effects, in a group of older adults (N = 230). Those engaged in general training did so with either the web-based brain game suite BrainHQ or the strategy video game Rise of Nations, while those trained on IADL skills completed instructional programs on driving and fraud awareness. Active control participants completed sets of puzzles. Comparing baseline and postintervention data across conditions, none of the preregistered primary outcome measures demonstrated a significant interaction between session and intervention condition, indicating no differential benefits. Analysis of expectation effects showed differences between intervention groups consistent with the type of training. Those in the IADL training condition did demonstrate superior knowledge for specific trained information (driving and finances). Twelve months after training, significant interactions between session and intervention were present in the primary measure of fraud detection, as well as the secondary measures of the letter sets task and Rey's Auditory Verbal Learning Test. However, the specific source of these interactions was difficult to discern. At 1-year follow-up those in the IADL condition did not maintain superior knowledge of driving and finances gained through training, as was present immediately postintervention. Hence, the interventions, when compared to an active control condition, failed to show general or specific transfer in a meaningful or consistent way. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Postoperative Analgesia and Palliative Care of Peng Block for Terminal Osteosarcoma.

Palliative care involves patients with a high incidence of chronic pain and inadequate treatment related to opioid abuse. In terminal patients, the side effects of opioids may result in lower quality of life due to their deleterious immunosuppression and gastrointestinal effects. In our routine clinical practice, we consider the ultrasound-guided PENG block as a palliative analgesic technique to improve end-of-life care to terminal patients.

A highly feasible, reliable, and fully remote protocol for mobile app-based cognitive assessment in cognitively healthy older adults.

Introduction: The early detection of cognitive impairment is one of the most important challenges in Alzheimer's disease (AD) research. The use of brief, short-term repeated test sessions via mobile app has demonstrated similar or better reliability and validity compared to standard in-clinic assessments in adult samples. The present study examined adherence, acceptability, and reliability for a remote, app-based cognitive screening protocol in healthy older adults. Methods: Cognitively unimpaired older adults (N = 52, ages 60-80) completed three brief cognitive testing sessions per day within morning, afternoon, and evening time windows, for 8 consecutive days using a mobile app-based cognitive testing platform. Cognitive tasks assessed visual working memory, processing speed, and episodic memory. Results: Participants completed an average of 93% (M = 22.3 sessions, standard deviation = 10.2) of the 24 assigned sessions within 8 to 9 days. Average daily adherence ranged from 95% of sessions completed on day 2 to 88% of sessions completed on day 8. There was a statistically significant effect of session time on adherence between the morning and afternoon sessions only F (1, 51) = 9.15, P  = .004, η p  2   = 0.152, with fewer afternoon sessions completed on average. The within-person reliabilities of average scores, aggregated across all 24 sessions, were exceptionally high, ranging from 0.89 to 0.97. Performance on the episodic memory task was positively and significantly associated with total score and word list recall score on the Telephone Interview for Cognitive Status. In an exit survey, 65% of participants reported that they "definitely" would complete the sessions again. Discussion: These findings suggests that remote, mobile app-based cognitive testing in short bursts is both highly feasible and reliable in a motivated sample of cognitively normal older adults. Limitations include the limited diversity and generalizability of the sample; this was a largely White, highly educated, and motivated sample self-selected for AD research.

Deep learning-based predictions of older adults' adherence to cognitive training to support training efficacy.

As the population ages, the number of older adults experiencing mild cognitive impairment (MCI), Alzheimer's disease, and other forms of dementia will increase dramatically over the next few decades. Unfortunately, cognitive changes associated with these conditions threaten independence and quality of life. To address this, researchers have developed promising cognitive training interventions to help prevent or reverse cognitive decline and cognitive impairment. However, the promise of these interventions will not be realized unless older adults regularly engage with them over the long term, and like many health behaviors, adherence to cognitive training interventions can often be poor. To maximize training benefits, it would be useful to be able to predict when adherence lapses for each individual, so that support systems can be personalized to bolster adherence and intervention engagement at optimal time points. The current research uses data from a technology-based cognitive intervention study to recognize patterns in participants' adherence levels and predict their future adherence to the training program. We leveraged the feature learning capabilities of deep neural networks to predict patterns of adherence for a given participant, based on their past behavior. A separate, personalized model was trained for each participant to capture individualistic features of adherence. We posed the adherence prediction as a binary classification problem and exploited multivariate time series analysis using an adaptive window size for model training. Further, data augmentation techniques were used to overcome the challenge of limited training data and enhance the size of the dataset. To the best of our knowledge, this is the first research effort to use advanced machine learning techniques to predict older adults' daily adherence to cognitive training programs. Experimental evaluations corroborated the promise and potential of deep learning models for adherence prediction, which furnished highest mean F-scores of 75.5, 75.5, and 74.6% for the Convolution Neural Network (CNN), Long Short-Term Memory (LSTM) network, and CNN-LSTM models respectively.

Depressive symptoms mediate the relationship between diabetes and cognitive performance in a community-based sample of older adults.

AIMS: To evaluate whether diabetes and prediabetes are associated with impaired cognitive performance among older adults and examine depressive symptoms as a mediator. METHODS: We used cross-sectional data from the Einstein Aging Study, a systematically recruited, community-based cohort study of diverse older adults (N = 794; Age Mean (SD) = 78.9 (5.3); 64.4% Non-Hispanic White, 28.7% Non-Hispanic Black, 5.7% Hispanic). Diabetes status was established via self-reported diagnosis, prescribed medications, and fasting blood glucose. Depressive symptoms were assessed using the Geriatric Depression Scale. Cognitive tests included Digit Symbol, Trails-B, Free Recall, Category Fluency, Boston Naming, and Block Design. Linear regression and mediation analyses were applied. RESULTS: Compared to those without diabetes, diabetes was associated with worse performance on all cognitive tests (ps < 0.05), except Trails-B (p = 0.53), and increased depressive symptoms (p < 0.01). For diabetes, mediation via increased depressive symptoms was observed for Free Recall (p = 0.044), Category Fluency (p = 0.033), and Boston Naming (p = 0.048). CONCLUSIONS: Diabetes was consistently associated with worse cognitive performance and increased depressive symptoms among this older cohort, while prediabetes was not. Mediation findings suggest depressive symptoms may be a biobehavioral pathway linking diabetes and cognition, though the temporal sequence is unclear. If causal, addressing both diabetes and depressive symptoms among older adults may protect cognitive function.

The where and when of COVID-19: Using ecological and Twitter-based assessments to examine impacts in a temporal and community context.

In March 2020, residents of the Bronx, New York experienced one of the first significant community COVID-19 outbreaks in the United States. Focusing on intensive longitudinal data from 78 Bronx-based older adults, we used a multi-method approach to (1) examine 2019 to early pandemic (February-June 2020) changes in momentary psychological well-being of Einstein Aging Study (EAS) participants and (2) to contextualize these changes with community distress scores collected from public Twitter posts posted in Bronx County. We found increases in mean loneliness from 2019 to 2020; and participants that were higher in neuroticism had greater increases in thought unpleasantness and feeling depressed. Twitter-based Bronx community scores of anxiety, depressivity, and negatively-valenced affect showed elevated levels in 2020 weeks relative to 2019. Integration of EAS participant data and community data showed week-to-week fluctuations across 2019 and 2020. Results highlight how community-level data can characterize a rapidly changing environment to supplement individual-level data at no additional burden to individual participants.

A New Tool for Assessing Older Adults' Wireless Network Proficiency: The Wireless Network Proficiency Questionnaire.

Interacting with information communication technologies (ICTs) often requires proficiency with wireless networks. For older adults especially, lacking proficiency can lock them out of the many benefits afforded by these devices. In an older adult sample (N = 203), we assessed reliability and validated a set of tools to assess wireless network proficiency: the Wireless Network Proficiency Questionnaire (WNPQ; 19 questions) and a short-form (WNPQ-8; eight questions). The WNPQ and WNPQ-8 were found to be reliable and valid, positively related to computer proficiency (r = .63 for both) and mobile device proficiency (r = .75, r = .74), and negatively related to age (r = -.21, r = -.23). WNPQ scores predicted whether participants could report their Wi-Fi credentials (odd ratio = 1.17-1.73). We conclude that the WNPQ may serve as a useful tool for facilitating wireless network training and assessing proficiency in research.

Investigating message framing to improve adherence to technology-based cognitive interventions.

A cognitive intervention study was conducted with the purpose of exploring methods to improve adherence to a technology-based cognitive intervention and uncover individual differences that predict adherence (N = 120). The study was divided into two phases: Phase 1, in which participants were asked to follow a prescribed schedule of training that involved gamified neuropsychological tests administered via tablet, and Phase 2, in which participants were asked to play as frequently as they wished. Positive- and negative-framed messages about brain health were delivered via the software program, and measures of cognition, technology proficiency, self-efficacy, technology attitudes, and belief in the benefits of cognitive training were collected. Generalized linear mixed-effects models revealed that positive-framed messages encouraged greater adherence over negative-framed messages, but this effect was restricted to Phase 2 of the study in the absence of social pressure. Measures of memory and self-efficacy demonstrated some, but limited, ability to predict individual differences in adherence. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Mindful walking and cognition in older adults: A proof of concept study using in-lab and ambulatory cognitive measures.

Mindfulness practice and walking have been linked individually to sustain cognition in older adults. This early-phase study aimed to establish proof-of-concept by evaluating whether an intervention that integrates light-intensity walking with mindfulness practices shows promising signs of improving cognition in older adults. Participants (N = 25, Mage = 72.4 ± 6.45) were community-dwelling older adults who engaged in a supervised mindful walking program over one month (8 sessions total, 2 sessions per week, 30-minute slow walking containing mindfulness skills). They completed performance-based and subjective ratings of cognitive measures in field before and after two mindful walking bouts using a smartphone app. They also completed in-lab performance-based and self-report cognitive measures at baseline and after the entire program. Controlling for demographics, potential covariates, and time trends, short-term improvements in perceived cognition and processing speed were observed from pre- to post-mindful walking sessions (i.e., 30 min) across multiple ambulatory cognitive measures (Cohen's ds range = 0.46-0.66). Longer-term improvements in processing speed and executive function were observed between baseline and end of the program (i.e., one month) across various performance-based cognitive measures (ds range = 0.43-1.28). No significant changes were observed for other cognitive domains. This early-phase study (Phase IIa) provides preliminary support that mindful walking activity is promising for sustaining cognition in older adults. Our promising findings form the building blocks of evidence needed to advance this intervention to a fully powered randomized controlled trial that examines program efficacy with a comparator. Favorable outcomes will inform the development of this lifestyle behavioral strategy for promoting healthy brain aging in late adulthood.